Magnetic resonance imaging (MRI) is a medical imaging modality that can create images of the inside of a human body without using x-rays or other ionizing radiation. MRI uses a powerful magnet to create a strong, uniform, static magnetic field (i.e., the “main magnetic field”). When a human body, or part of a human body, is placed in the main magnetic field, the nuclear spins that are associated with the hydrogen nuclei in tissue water become polarized. This means that the magnetic moments that are associated with these spins become preferentially aligned along the direction of the main magnetic field, resulting in a small net tissue magnetization along that axis (the “z axis,” by convention). A MRI system also comprises components called gradient coils that produce smaller amplitude, spatially varying magnetic fields when current is applied to them. Typically, gradient coils are designed to produce a magnetic field component that is aligned along the z axis and that varies linearly in amplitude with position along one of the x, y or z axes. The effect of a gradient coil is to create a small ramp on the magnetic field strength, and concomitantly on the resonance frequency of the nuclear spins, along a single axis. Three gradient coils with orthogonal axes are used to “spatially encode” the MR signal by creating a signature resonance frequency at each location in the body. Radio frequency (RF) coils are used to create pulses of RF energy at or near the resonance frequency of the hydrogen nuclei. These coils are used to add energy to the nuclear spin system in a controlled fashion. As the nuclear spins then relax back to their rest energy state, they give up energy in the form of an RF signal. This signal is detected by the MRI system and is transformed into an image using a computer and known reconstruction algorithms.
One technique that has been developed to accelerate MR data acquisition is commonly referred to as “parallel imaging” or “partial parallel imaging.” In parallel imaging, multiple receive coils acquire data from a region or volume of interest, where the data is undersampled, for example, in a phase-encoding direction so that only a fraction of k-space data is acquired in an image scan. Thus, parallel imaging is used to accelerate data acquisition in one or more dimensions by exploiting the spatial dependence of phased array coil sensitivity. Parallel imaging has not only been shown to be successful in reducing scan time, but also reducing image blurring and geometric distortions. Moreover, parallel imaging can be used to improve spatial or temporal resolution as well as provide increased volumetric coverage.
There are several types of parallel imaging reconstruction methods that have been developed to generate the final, unaliased image from accelerated data. One such group of methods is data driven or autocalibrating techniques, such as Automatic Simultaneous Acquisition of Spatial Harmonics (AUTO-SMASH), Generalized Autocalibrating Partially Parallel Acquisition (GRAPPA), and Autocalibrating Reconstruction for Cartesian Sampling (ARC), among others. Such techniques calculate reconstruction weights necessary to synthesize unacquired data directly from acquired data in k-space using an algorithm that does not require coil sensitivity estimates. In such autocalibration based techniques, the reconstruction weights are calculated from a small amount of fully sampled calibration data that is typically embedded within the scan (i.e., “self-calibration” or “auto-calibration”), but can also be acquired before or after the scan.
More recently, another technique for accelerating MR data acquisition known as “compressed sensing” has been developed. Compressed sensing originates from the observation that most medical images have some degree of “compressibility.” That is, when transformed into some suitable domain such as a wavelet domain, a substantial number of values can be set to zero (i.e., compressed) with little loss of image quality. In compressed sensing, compressed images are reconstructed using a non-linear reconstruction scheme, such as an L1-norm constraint, wherein the undersampled artifacts in the chosen domain must be sufficiently sparse (or incoherent) to effectively reconstruct the image. Like parallel imaging, compressed sensing has been found to reduce scan time, image blurring, and geometric distortions.
As both parallel imaging and compressed sensing enable accelerated MR data acquisition, there have been previous efforts to combine parallel imaging with compressed sensing. More specifically, efforts have been made to combine the two techniques by including the parallel imaging technique as a data consistency constraint in the compressed sensing reconstruction, thus resulting in a simultaneous implementation of the techniques. However, by incorporating parallel imaging as a data consistency constraint, the computational efficiency of the compressed sensing reconstruction is greatly reduced, thereby negating some of the benefits provided by using the parallel imaging or compressed sensing technique individually.
It would therefore be desirable to have a system and method that combines parallel imaging with compressed sensing that increases computational efficiency, so as to generate a high-quality reconstructed image while also reducing scan time.